Systems for identifying persons through intrinsic human traits have been developed. These systems operate by taking images of a physiological trait of a person and comparing information stored in the image to data that corresponds the trait for a particular person. When the information stored in the image has a high degree of correlation to the relevant data previously obtained a particular person's trait, a person can be positively identified. These biometric systems obtain and compare data for physical features, such as fingerprints, voice, and facial characteristics. Different traits impose different constraints on these systems. For example, fingerprint recognition systems require the person to be identified to contact an object directly for the purpose of obtaining fingerprint data from the object. Facial feature recognition systems, however, do not require direct contact with a person and these biometric systems are capable of capturing identification data without the cooperation of the person to be identified.
One trait especially suited for non-cooperative identification is an iris pattern in a person's eye. The human eye iris provides a unique trait that changes little over a person's lifetime. For cooperative iris recognition, the person to be identified is aware of an image being taken and the captured image is a frontal view of the eye. Non-cooperative iris image capture systems, on the other hand, obtain an iris image without a person's knowledge of the data capture. Thus, the subject's head is likely moving and his or her eyes are probably blinking during iris image acquisition. Consequently, the captured image is not necessarily a frontal view of the eye.
Identification of a person from an iris image requires iris image segmentation. Segmentation refers to the relative isolation of the iris in the eye image from the other features of an eye or that are near an eye. For example, eyelashes and eyelids are a portion of an eye image, but they do not contribute to iris information that may be used to identify a person Likewise, the pupil does not provide information that may be used to identify a person. Consequently, effective segmentation to locate the portions of a captured eye image that contain iris pattern information is necessary for reliable identification of person. Because previously known iris identification systems rely upon the acquisition of eye images from cooperative subjects, iris segmentation techniques have focused on frontal eye images. What is needed is a more robust method of iris segmentation to identify correctly those portions of an eye image that contain iris pattern data in an eye image obtained from a non-cooperative eye image acquisition system.
To address some of the issues related to the capture of quality eye images in a non-cooperative environment, video-based methods of image acquisition have been used. Multiple frames of eye image data provide more information that is useful for overcoming limitations present in a non-cooperative environment. All frames of eye image data, however, are not useful for identification purposes. For example, some frames may be out of focus, or contain low contrast images. Additionally, movement of the subject's head or eyes may result in the image of a closed eye from blinking or no eye at all because the subject turned his or her head. These problems with frame data require that the quality of the image data and the accuracy of the segmentation be verified. Otherwise, recognition accuracy may be drastically reduced. Previously known image quality assessment methods, unfortunately, are directed to individual iris images and do not adequately evaluate image data on a frame basis in a video sequence. Additionally, no methods for iris segmentation evaluation are currently known. Thus, development of image quality assessment methods for video frame data of eye images and segmentation evaluation methods is desirable.